Common Probably Ditribution In Simulation
Common probability distribution in simulation (cont.):
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Gamma distribution:
Failure due to repetitive,disturbances Duration of a multiphase,task |
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The following are some prerequisites for running simulation experiments and optimization studies:
1. Understanding the type of simulation (terminating or steady state)
2. selecting the system performance measure(s), such as throughput, lead time, cost, and utilization
3. Selecting model control and random factors that affect model performance and representing them statistically in the model (examples include arrival rate, cycle times, failure rate, available resources, speeds)
Modeling and Statistical Methods in Simulation Analysis:
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Modeling Activity |
Statistical Methods |
Modeling Skills |
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Input modeling |
1. Sampling techniques 2. Probability model 3. Histograms 4. Theoretical distribution 5. Parameter estimation 6. Goodness of fits 7. Empirical distribution |
1. Data collection 2. Random generation 3. Data classification 4. Fitting distributions 5. Modeling variability 6. Conformance test 7. Using actual data |
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Model running |
1. Model type 2. Transient time 3. Data collection 4. Sample size |
1. Steady state 2. Warm up period 3. Run length 4. Replications 5. Recording response |
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Output analysis |
1. Graphical tools 2. Descriptive statistics 3. Inferential statistics 4. Experimental design 5. Optimization search |
1. Output representation 2. Result summary 3. Drawing inferences 4. Design alternatives 5. Best design
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